Osteoporosis is a skeletal disorder which leads to bone mass loss and diminished bone strength (as characterized by density and quality), and hence to an increased fracture risk. Bone loss typically appears due to a deficiency of estrogen in postmenopausal women, or due to other age related mechanisms (e.g. secondary hyperparathyroidism, reduced mechanical loading, etc.). Since effective drug therapies are available, it is crucial to correctly diagnose the onset of osteoporosis so as to minimize the future risk of fractures.
Approximately 22.1% of the female population, and 6.6% of the male population older than 50, are affected by osteoporosis and the percentages increase with age. Of the bone fractures caused by osteoporosis, the hip fractures are typically the most devastating ones for both the patient and society: the vast majority of patients require hospitalization and surgery. Literature reports have shown that in case of hip fractures the mortality rate increases in the first year by 8-36%. Specifically, 20% of the patients die within one year, whereas another 20% require permanent nursing home care.
A total of 3.7 million osteoporotic fractures were estimated in 2000, whereas approx. 24% represented hip fractures. Crucially, healthcare costs were estimated at a level of €36.2 billion, whereas hip fractures caused two-thirds of the expenses. Furthermore, osteoporosis related healthcare costs are expected to more than double to around €76 billion by 2050, since the estimated number of hip fractures alone is expected to increase to 6.3 million by 2050. Typically, vertebral fractures lead to fewer complications, but they are also more frequent, and previous clinical data suggests that only around 30% of them are diagnosed. Importantly, once a patient suffers a vertebral fracture, the risk for subsequent fractures increases tenfold. Besides hip and vertebral fractures, wrist fractures are the most common osteoporotic fractures, but since osteoporosis is a condition which affects the entire skeleton there is an increased fracture risk for almost any bone in the human body.
Furthermore, it has been shown that women with osteoporosis suffer a 4% decrease in bone strength per year. Thus, as mentioned above, the fracture risk increases with age, an aspect which is amplified by the age-related increase in the risk of falling. A longitudinal study was performed based on volumetric quantitative computed tomography and finite element analysis to determine the changes in bone strength. Both normal load conditions and fall related load conditions were considered. Over an average interval of five years aBMD decreased by 4%, and bone strength decreased by 4% to 12%, depending on the gender and the load conditions (more pronounced decrease for women and for fall related load conditions).
Bone is a living material which can regenerate, and whose structure and density are adapted as a result of the mechanical forces acting on it. Specifically, the bone architecture is optimized so as to resist forces with the smallest possible amount of material.
Two important characteristics when describing the mechanics of bone under loading conditions are strain (describing the deformation of the bone in comparison to its initial state) and stress (describing the internal forces of the bone). Depending on the values of these two quantities, the bone is in the pre-yield, post-yield, or ultimate state. In the pre-yield state, deformations are purely elastic; that is, any deformation is temporary and the initial state of the bone is reached once the load is removed. The most important material property for this state is the Young's modulus (stiffness or modulus of elasticity). It is the slope of the stress-strain curve, and represents the pressure which is required to act upon a bone to obtain a certain deformation. Once the yield point is surpassed, elastic deformation becomes plastic deformation, and permanent deformations are the result. The yield point is characterized by yield strain, yield stress and yield strength. Bone toughness is also used sometimes and it represents the total energy which the bone can absorb before it passes the yield point. The ultimate state is described by the ultimate strain, the ultimate stress and the ultimate strength.
Bone material is encountered in the human body either as cortical/compact bone or as cancellous/trabecular bone. On average, 80% of the bone mass is cortical and these regions of the bone ensure its stability. Cancellous bone typically resides inside of a frame formed of cortical bone and has a sponge like appearance. Its density is much smaller than that of cortical bone.
Routine clinical workflows for evaluating the risk of fracture currently rely on dual-energy X-ray absorptiometry (DXA). The main quantity extracted from DXA is the areal bone mineral density (aBMD), which was linked to an increased risk of hip fracture. However, BMD is only one facet responsible for increased fragility. Studies have shown that decreased Bone Mineral Density (BMD) determined by DXA, independent of ethnic background, sex, or age is related to an increased risk of hip fracture. DXA however has a series of disadvantages. For example, because it is two-dimensional, DXA cannot properly distinguish differential changes of cortical and cancellous bone. It has been shown that in case of many osteoporotic fractures the bone was not characterized as being osteoporotic by the DXA scan. Specifically, the sensitivity of DXA-based decision making is too low. Bone mineral content (BMC) is sometimes used as an alternative to BMD.
Recent research has demonstrated that biomechanical modeling techniques, based on finite element analysis (FEA), bear great potential of improving clinical decision making processes. These techniques combine geometrical information extracted from medical imaging (structural properties, anatomical shape) with background knowledge on the patient (e.g., demographics) and patient-specific information (e.g. loads), encoded in a complex mathematical solid mechanics model consisting of partial differential equations which can be solved only numerically. This approach leads to a large number of algebraic equations, making it computationally very demanding. Typically, the solution of these models requires a time frame from a few minutes to a few hours for high-fidelity models representing the complete three dimensional space.
Since the FEA analysis is typically based on CT imaging data, it is sometimes also called biomechanical CT (BCT). When performing BCT, the gray-scale voxels in the CT DICOM image are converted to calibrated values of BMD. Bone is segmented from the data and used to create a finite element model (FEM). By applying an FEM solver, a stress analysis may be performed to determine measures of interest related to the strength of the bone. Several measures of interest may be derived from such an analysis including whole-bone strength, load-to-strength ratio, the type of fracture, average stress, average strain, and stiffness.
In-vitro studies performed on cadaver bones have demonstrated that BCT provides superior estimates of vertebral and femoral bone strength, as compared to DXA-based aBMD. Patient-specific FEA was performed for 12 cadaver femur bones in a double blinded fashion by two different research groups to determine numerical errors but also errors with respect to experimental findings, as determined from in-vitro experiments. Numerical results indicated the computations are robust and match well experimental findings.
In one of the first clinical studies it was shown that vertebral strength, as assessed by the stress, is superior compared to BMD for the assessment of osteoporotic fracture risk. More recently the utility of BCT was confirmed in a patient cohort for which aBMD was not able to differentiate between patients with and without fractures. This finding was also confirmed in a study focused on the femur bone.
It has been concluded that FEMs can predict femoral fracture loads more accurately than other methods. On the other hand, FEA accuracy depends on the modeling methodology, and requires further standardization.
Since during an FEA simulation the strain may increase beyond the yield strain of the bone material, typically a von Mises strain criterion is employed: a small Young's modulus (e.g., 0.01 MPa), is assigned to all finite elements which have a strain larger than the yield strain.
A retrospective study on 1110 individuals, which employed an FEA based assessment of bone strength, indicated that fracture prediction can be performed for women by combining femoral strength with femoral neck areal BMD.
The effect of scanner settings on FEA results was recently evaluated in a study on cadaver bones: both low and high resolution scanner settings were considered. Strength and stiffness values estimated from data acquired with the two imaging setups differed, indicating that the robustness of this method needs to be increased.
In a very recent study, digital volume correlation analysis of micro-computed tomography images acquired during compression and flexion of spine segments, were used to measure displacements, which were then compared to FEA results. The computational results were able to partially capture the vertebral failure patterns, but further work is required to obtain accurate predictions of failure mechanisms from FEA.
Furthermore, BCT may be employed to gain further insight into the bone mechanics, by changing the anatomical models and/or its properties and rerunning the FEA. For example, the outer layer of the bone may be removed, to determine the variation in bone strength.
One of the disadvantages is that BCT requires a CT, which is associated with a higher cost and radiation compared to a DXA scan. However, BCT may be applied based on images acquired during previous CT scans or acquired during CT scans performed for a different scope (lung cancer screening, a cardiac CT for ruling out coronary artery disease, abdominal/pelvic region, etc.). Given the fact that millions of CT scans are performed per year, BCT bears a great potential, and would actually be more convenient than a DXA scan. Very recently it was shown that CT images acquired for colonography are suitable for performing FEA based BCT analysis, without requiring changes in the imaging protocol.
However, central quantitative CT is currently not an established imaging method for diagnosing patients based on BMD as being osteopenic or osteoporotic. Nevertheless, volumetric exclusively cancellous BMD may be employed to assess therapies. Currently, quantitative CT is recommended as an alternative to DXA if: (i) the individual is very large or very small, (ii) the individual is expected to have advanced degenerative disease in the lumbar spine, and (iii) monitoring of metabolic bone change is required.
Importantly, the clinical potential of BCT analysis for spine and hip has been recently acknowledged by the International Society of Clinical Densitometry. It has been noted that the usage of BCT in routine clinical practice still has to overcome a few challenges related to the availability of FEA software and integration in the clinical workflow. As described above, a significant opportunity is the secondary use of CT scans for the assessment of risk of fracture and osteoporosis diagnosis (use CT scans performed originally for the chest, abdominal and pelvic regions). Alternatively, it has been shown that 3-T MRI of femur microarchitecture can also be used to assess the strength of a bone based on FEA. The major disadvantage of FEA is that at least a few hours are required to perform a detailed analysis, which may represent a too large timeframe for a routine clinical workflow.
The World Health Organization (WHO) has introduced Fracture Risk Assessment Tool (FRAX) as a diagnostic tool to estimate the risk of bone fracture over a 10 year time span. FRAX is based on BMD assessed at the femoral neck and estimated fracture probabilities for the hip or for other osteoporosis relevant locations (spine, forearm, shoulder). This tool further incorporates information related to age, ethnicity, sex, weight, height, fracture history, smoking, alcohol, glucocorticoids, and rheumatoid arthritis. The accuracy of the FRAX score has been shown to be limited, with an area under the curve (AUC) of 0.56 to 0.69.
Before FRAX, a grading system was developed specifically for osteoporotic vertebral fractures, which, amongst others, is recommended by ISCD, the International Osteoporosis Foundation, and the European Society of Skeletal Radiology. This system defines a vertebral fracture as a vertical deformation of more than 20% and a reduction of the height area of 10-20%, and three different fracture grades are defined (mild, moderate and severe). The approach was tested in several clinical studies.
One alternative proposed in literature is 3D statistical shape and appearance modeling. It uses a database of 3D bone images and is based on the average shape and density distribution and their main modes of variation. To determine the shape and density of a new patient-specific bone, one only needs to determine the contribution of the main modes to the shape and distribution of the current bone. Thus a 2D image, as acquired through DXA may be used, followed by a 2D-3D matching algorithm which outputs the weights of the contributions. This methodology circumvents the usage of Quantitative Computed Tomography (QCT) and image segmentation. An FEA analysis may be performed based on the resulting 3D model.
Recently, a methodology based on machine learning (ML) was employed to predict measures of interest extracted from FEA (i.e. stress). A database of 89 femur bones was used, and features were based on a statistical shape model, and morphometric and density information. The distribution of stress was predicted with a correlation of 0.98.
Another important application for DXA/BCT in the context of bone material analysis is the early evaluation of drug-based treatment in terms of their efficiency, especially in women with osteoporosis. As in the case of risk of fracture evaluation, aBMD is not able to comprehensively assess the efficiency of a drug-based treatment and BCT has shown to provide superior discriminatory power. BCT leads to increased statistical power and improved insight into the treatment effect.
One reason for the discrepancies between aBMD and BCT is that for some treatment plans the cortical bone density increased while the trabecular bone density decreased, leading to an overall approximately constant aBMD value. From a biomechanical point of view, however, the bone strength increased in these cases.
One possible drug treatment for osteoporotic patients employs teriparatide: the trabecular bone volume is increased, thus improving the structure of the bone, and also the thickness of the cortical layer is increased. Alendronate is another drug used to treat osteoporotic patients: a study has shown that it leads to similar improvements in femoral strength as teriparatide. In another recent study it was shown that teriparatide increased both vertebral and femoral strength, as assessed by FEA performed based on quantitative CT scans.
BCT may also be used to assess the effect of changes in treatment plans. Previous studies have shown that in case of treatment plans based on alendronate or raloxifene, the addition or the switch to teriparatide lead to different outcomes as assessed by aBMD. In a recent study, BCT was employed to further evaluate these two strategies and to additionally also compute vBMD (volumetric BMD) from quantitative CT. It was shown that in women with osteoporosis the addition and the switch to teriparatide lead to similar outcomes for the spine strength. Regarding the hip strength, it increased more in the group of teriparatide addition.
In another study, a different drug treatment plan based on denosumab was evaluated using BCT analysis. It was shown that the strength of both trabecular and cortical bone improved in time as assessed through FEA.
Romosozumab is yet another drug used for patients with high risk of bone fracture: it boosts bone formation and inhibits bone resorption. Linear FEA was recently employed to assess the bone stiffness in a longitudinal study of a treatment plan based on Romosozumab: the whole bone stiffness increased significantly and rapidly after three months of drug administration.
Over the last years, biomarkers have received increased attention for the assessment of bone strength and fracture risk. Typically these markers are called bone turnover markers (BTM). These are based on the evaluation of proteins and enzymes which are generated during processes of bone formation and bone resorption. Typically used markers of bone formation include serum total alkaline phosphatase, serum bone-specific alkaline phosphatase, serum osteocalcin, and serum type 1 procollagen (C-terminal/N-terminal). Typically used markers of bone resorption are urinary hydroxyproline, urinary total pyridinoline (PYD), urinary free deoxypyridinoline (DPD), urinary collagen type 1 cross-linked N-telopeptide (NTX), urinary or serum collagen type 1 cross-linked C-telopeptide (CTX), bone sialoprotein (BSP), and tartrate-resistant acid phosphatase 5b.
BMTs are currently not used routinely in the assessment of osteoporosis; however several studies have shown that the average values of these markers differ between groups of osteoporotic patients and healthy subjects. Furthermore, it has been shown the biomarkers may also be used to assess the bone strength: a recent study validated the biomarker P1NP (amino-terminal-propeptide of type I collagen) as a good predictor of bone strength, as determined from FEA.
Other measures to be used as biomarkers may be extracted from different imaging techniques, like ultrasound: broadband ultrasound attenuation (BUA), speed of sound (SOS), and the resulting stiffness value and quantitative ultrasonometry (QUS) index.
In conclusion, three different categories of markers have shown to be linked to the risk of bone fracture: BMD (areal, volumetric, etc.), FEA based markers, and BTM.